Update renovation.py

renovation.py

@@ -3,6 +3,7 @@ import random
 import datasets
 import requests
 import os
+import py7zr  # for extracting .7z files
 
 from PIL import Image
 from io import BytesIO
@@ -24,7 +25,11 @@ _DESCRIPTION = """\
 This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
 """
 
-_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv"
+_URLS = {
+    "cheap": "https://huggingface.co/datasets/rshrott/renovation/raw/main/cheap.7z",
+    "average": "https://huggingface.co/datasets/rshrott/renovation/raw/main/average.7z",
+    "expensive": "https://huggingface.co/datasets/rshrott/renovation/raw/main/expensive.7z",
+}
 
 _NAMES = ["cheap", "average", "expensive"]
 
@@ -50,54 +55,55 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        csv_path = dl_manager.download(_URL)
-        with open(csv_path, "r") as f:
-            reader = csv.reader(f)
-            next(reader)  # skip header
-            rows = list(reader)
-        
-        # Shuffle rows
-        random.shuffle(rows)
+        # Download and extract images
+        image_paths = []
+        for label, url in _URLS.items():
+            download_path = dl_manager.download(url)
+            extract_path = dl_manager.extract(download_path)
+
+            # Get image paths
+            for root, _, files in os.walk(extract_path):
+                for file in files:
+                    if file.endswith(".jpeg"):  # Assuming all images are .jpeg
+                        image_paths.append((os.path.join(root, file), label))
+
+        # Shuffle image paths
+        random.shuffle(image_paths)
 
         # 80% for training, 10% for validation, 10% for testing
-        train_end = int(0.8 * len(rows))
-        val_end = int(0.9 * len(rows))
+        train_end = int(0.8 * len(image_paths))
+        val_end = int(0.9 * len(image_paths))
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "rows": rows[:train_end],
+                    "rows": image_paths[:train_end],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "rows": rows[train_end:val_end],
+                    "rows": image_paths[train_end:val_end],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "rows": rows[val_end:],
+                    "rows": image_paths[val_end:],
                 },
             ),
         ]
 
     def _generate_examples(self, rows):
-        def url_to_image(url):
-            response = requests.get(url)
-            img = Image.open(BytesIO(response.content))
+        def file_to_image(file_path):
+            img = Image.open(file_path)
             return img
     
-        for id_, row in enumerate(rows):
-            if len(row) < 2:
-                print(f"Row with id {id_} has less than 2 elements: {row}")
-            else:
-                image_file_path = str(row[0])
-                image = url_to_image(image_file_path)
-                yield id_, {
-                    'image_file_path': image_file_path,
-                    'image': image,
-                    'labels': row[1],
-                }
+        for id_, (image_file_path, label) in enumerate(rows):
+            image = file_to_image(image_file_path)
+            yield id_, {
+                'image_file_path': image_file_path,
+                'image': image,
+                'labels': label,
+            }